Development of multivariate ionospheric TEC forecasting algorithm using linear time series model and ARMA over low-latitude GNSS station

Abstract

Forecasting of ionospheric time delays has become a significant importance in satellite based navigation and communication system applications. Several researchers have been developed and implemented univariate Total Electron Content (TEC) forecasting models successfully over low, mid and high latitude regions. Therefore, identifying an effective multivariate forecasting technique is very essential to alert the Global Navigation Satellite System (GNSS) users under various space weather conditions. In this paper, a new multivariate ionospheric TEC forecasting model based on linear time series model in combination with Autoregressive and Moving Average (ARMA) is proposed and implemented using Bengaluru International GNSS Service (IGS) station data (geographic lat. −13.02°N, long. 77.57°E; geomagnetic latitude: 4.4°N) during the period of 8 years (2009–2016) in the 24th solar cycle. The major factors, namely, geomagnetic activity (Ap), solar Extreme Ultraviolet (EUV) irradiance (F10.7p), periodic oscillations (annual, semi-annual, terannual and biennial oscillations) and long-term trend are considered in the model as input parameters along with real time TEC observations. The proposed model is twofold: first, the impact of solar, geomagnetic, trend and periodic factors on TEC has been investigated from linear model. Second, ARMA method is applied for forecasting each factor. The forecasted individual factors are combined to obtain the forecasted TEC values. The estimated TEC from the proposed model has good agreement with the observed Global Positioning System (GPS) – TEC. It is noticed that the magnitudes of semi-annual variation have been reflected to be high during the High Solar Activity (HSA) period. It is also found that the geomagnetic effect on TEC is relatively low. The proposed multivariate ionospheric TEC forecasting model would be useful for characterizing the low-latitude ionospheric variations under various space weather conditions.